https://ph02.tci-thaijo.org/index.php/TJOR/issue/feed Thai Journal of Operations Research : TJOR 2021-08-09T11:23:48+07:00 Associate Prof Dr.Kannapha Amaruchkul orjournal.th@gmail.com Open Journal Systems วารสารไทยการวิจัยดำเนิงาน https://ph02.tci-thaijo.org/index.php/TJOR/article/view/243908 A Study of Milk-Run Vehicle Routing Problem and Receiving Schedule Adjustment A Case Study of Just-In-Time Automobile Factory ABC 2021-08-09T11:23:37+07:00 Paranut Kunwimol paranut.kun@stu.nida.ac.th Sarawut Jansuwan sarawut@as.nida.ac.th <p>The main objectives of this study is to improve the milk run vehicle routing of the Just-In-Time automobile factory &nbsp;. The milk run vehicles normally travel to pick up automobile parts from suppliers in Chonburi and Rayong to factory located in Leam Chabang Industrial estate, Chonburi. Two vehicle routing problem (VRP) methods are studied including Saving Algorithm and Metaheuristics in VRP Spreadsheet Solver. The results obtained from these two methods are compared and selected for the best alternatives. The results show that the saving algorithm can reduce the transportation cost from 21,859,083 Baht/Year to 12,769,159 Baht/Year, or can save 9,089,924 Baht/Year (41.58%), while the Metaheuristics can reduce the transportation cost to 12,769,159 Baht/Year, or can save 11,962,011 Baht/Year (45.28 %). Further, the schedule of truck arrival are also improved using the recent constraints of operational time of the factory and the folk lift resource available during the slot. The purposed method could alleviate the congestion of truck arrivals during peak period in the factory effectively.</p> 2021-08-06T11:37:21+07:00 Copyright (c) 2021 Thai Journal of Operations Research : TJOR https://ph02.tci-thaijo.org/index.php/TJOR/article/view/243919 Forecasting Export Values of Cars, Equipment and Parts of Thailand by Time Series Methods 2021-08-09T11:23:39+07:00 Uracha Chantrapha urc.teay@gmail.com Nantachai Kantanantha nantachai.k@chula.ac.th <p>This research aimed to study the forecasting methods for the export values of cars, equipment and parts of Thailand by time series methods including moving average, Holt-Winters exponential smoothing and SARIMA. The monthly export value data were gathered from Office of the Permanent Secretary Ministry of Commerce from January 2008 to December 2019, a total of 144 months. The data were divided into two sets. The first set was the data from January 2008 to December 2018 and was used to develop the forecasting models. The second set was the data from January 2019 to December 2019 and was used to compare the forecast accuracy by the mean absolute percentage error (MAPE). The results of study showed that the SARIMA method had MAPE at 10.18% while the Holt-Winters exponential smoothing and the moving average methods that had MAPEs at 10.38% and 11.06%, respectively. Thus, the SARIMA method was the most appropriate and had the highest accuracy on forecast of the export values of cars, equipment and parts of Thailand.</p> 2021-08-06T11:38:04+07:00 Copyright (c) 2021 Thai Journal of Operations Research : TJOR https://ph02.tci-thaijo.org/index.php/TJOR/article/view/243959 Use of Association Rules Bases Storage Assignment Location for Improving Class-Based Warehouse Design: Case Study A Packaging Company 2021-08-09T11:23:41+07:00 Akkaranan Pongsathornwiwat akkaranan@as.nida.ac.th Akkadet Ubonsai akkadet10@gmail.com <p>The objective of this research is to improve the efficiency of a warehouse of a packaging company by re-assignment of storage location. As observed the company’s exiting problems, there is an issue on the inappropriate storage locations, causing problems of unnecessary travel distances and delays shipping as well as additional costs of overtime. To overcome this, the study formulated the problem as storage location re-assignment problems. The revised product placements and locations are applied a concept of association rules to find the relationships between items that be frequently ordered together and make them as the new product family, ranging from high-, medium-, and low movements. The proposed linear programming problem is solved by Fast-Mover-Closet-to-Door concepts with Open Solver in Microsoft Excel as the solver tool to find the optimal solution. The result shows that the new product location and layout can significantly help improving the warehouse efficiency in terms of decreasing picking distances by 30.40%, reducing the period of overtime hiring by 90.76% and reducing overtime costs by 50,982 baht a year approximately. Furthermore, it can improve the labor productivity of normal picking times up to 6.82%.</p> 2021-08-06T11:40:44+07:00 Copyright (c) 2021 Thai Journal of Operations Research : TJOR https://ph02.tci-thaijo.org/index.php/TJOR/article/view/243965 Application of Bi-level Decision-Making Technique to manage Inventory and Transportation System: Case Study of Wholesaler in Chiang Mai Province 2021-08-09T11:23:43+07:00 Tinnakorn Phongthiya tinnakorn.phongthiya@cmu.ac.th Pornvisa Tharakhum p_tharakhum@hotmail.com Chompoonoot Kasemset chompoonoot.kasemset@cmu.ac.th <p>The objective of this research was to apply a bi-level decision-making technique to manage inventory system and transportation scheduling in a case study of a wholesaler in Chiang Mai Province. The first level of the decision-making is to manage inventory system. Techniques, including a Multiple Criteria Decision-Making (MCDM) and an Analytical Hierarchy Process (AHP), were used to classify the products of the case study into three groups, including A, B, and C where products in group A are the most important while products in group B and C are less important, respectively. The appropriate inventory ordering policies for each product group were then selected considering their importance and value. The second level of the decision-making is to manage a transportation scheduling based on the importance and inventory ordering policies of each product group and using a Priority Queuing (PC) technique. The results of this research were compared with the actual data of the case study. It showed that the application of bi-level decision-making to manage inventory system and transportation scheduling of the case study can reduce inventory management cost by approximately 19.69 percent and the number of transportation cycles 12 cycles in three months.</p> 2021-08-06T11:41:43+07:00 Copyright (c) 2021 Thai Journal of Operations Research : TJOR https://ph02.tci-thaijo.org/index.php/TJOR/article/view/243968 Analysis of clusters among the Retirement Mutual Funds before and during the COVID-19 crisis period 2021-08-09T11:23:45+07:00 Suda Tragantalerngsak tragantalerngsa_s@silpakorn.edu Sasiprapa Hiriote Hiriote_s@silpakorn.edu Tanai Boonfueang boonfueang_t@silpakorn.edu Jeeranun Akawannung akawannung_j@silpakorn.edu <p>The objectives of this paper are to investigate the clusters of the Retirement Mutual Fund (RMF), using &nbsp;Multidimensional Scaling (MDS) method with their Treynor ratio measurements &nbsp;and to compare the structure of those clusters before the COVID-19 crisis period (year 2017-2019) and during the COVID-19 crisis period (year 2020). The results illustrated that the map from MDS indicated the different clusters of RMF funds according to their investment policy.&nbsp; Before the COVID-19 crisis period, the RMF funds are grouped into 3 clusters. The first cluster is the group of the RMF funds which mainly invest in thai capital market and government bond.&nbsp; The second and the third consist of the feeder funds or the funds that invest in foreign capital market.&nbsp; During the COVID-19 crisis period, the RMF funds are also able to group into 3 clusters. The first one is the group that invests in Thai capital market and government bond. The second and the third are the general feeder funds and the feeder funds that invest in healthcare sector in all of the world capital market, respectively.</p> 2021-08-06T11:42:28+07:00 Copyright (c) 2021 Thai Journal of Operations Research : TJOR https://ph02.tci-thaijo.org/index.php/TJOR/article/view/244008 A Comparison of Forecasting Methods for Seasonal Time-series with Many Zeros 2021-08-09T11:23:46+07:00 Thassakorn Sawetsuthipan thassakorn.sa@ku.th Peerayuth Charnsethikul thassakorn.sa@ku.th <p>The research aims to analyze the time series of daily wildfires area in Chiang Mai which are classified as seasonality, and the data also has a wide range of zero values over a number of periods which can result in high variance.&nbsp; The data used consists of two parts: the number of wildfires area and the climate which can be used as a factor that can affect the number of wildfire area. The&nbsp; obtained data used to create the appropriate forecasting model by comparing the six forecasting methods which are multiple regression with categorical variable, multivariate polynomial regression, the truncated fourier series, Holt-Winters’s additive method, SARIMAX methods by Box-Jenkins, and artificial neural network; in addition, to measure the efficiency of the model. The test data is divided into four phases: 3 months, 6 months, 1 year and 1 year 6 months, and compare with the six forecast methods by root mean square error (RMSE). The results showed that the Multiple Regression with Categorical Variable provided the lowest RMSE values for test data over a period of 3 months, 6 months, and 1 year, and SARIMAX method provided the lowest RMSE values for test data over a period of 1 year and 9 months.</p> 2021-08-06T11:43:30+07:00 Copyright (c) 2021 Thai Journal of Operations Research : TJOR